Overview

Brought to you by YData

Dataset statistics

Number of variables27
Number of observations5205
Missing cells14591
Missing cells (%)10.4%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory3.1 MiB
Average record size in memory625.5 B

Variable types

Text4
Numeric20
Categorical3

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
52 Weeks High is highly overall correlated with 52 Weeks Low and 7 other fieldsHigh correlation
52 Weeks Low is highly overall correlated with 52 Weeks High and 9 other fieldsHigh correlation
Chiffre d'affaires is highly overall correlated with 52 Weeks High and 12 other fieldsHigh correlation
Currency is highly overall correlated with Chiffre d'affaires and 4 other fieldsHigh correlation
Dividend Per Share Annual is highly overall correlated with 52 Weeks Low and 5 other fieldsHigh correlation
EBITDA is highly overall correlated with 52 Weeks High and 10 other fieldsHigh correlation
EPS Annual is highly overall correlated with 52 Weeks High and 10 other fieldsHigh correlation
Market Cap (in M) is highly overall correlated with 52 Weeks High and 11 other fieldsHigh correlation
Performance (52 weeks) is highly overall correlated with PriceHigh correlation
Price is highly overall correlated with 52 Weeks High and 10 other fieldsHigh correlation
Price 52 Weeks Ago is highly overall correlated with 52 Weeks High and 9 other fieldsHigh correlation
ROI Annual is highly overall correlated with 52 Weeks Low and 7 other fieldsHigh correlation
Résultat net is highly overall correlated with 52 Weeks High and 9 other fieldsHigh correlation
Total assets is highly overall correlated with Chiffre d'affaires and 3 other fieldsHigh correlation
Volume 1 month is highly overall correlated with Chiffre d'affaires and 3 other fieldsHigh correlation
Volume 52 weeks is highly overall correlated with Chiffre d'affaires and 3 other fieldsHigh correlation
Currency is highly imbalanced (91.6%)Imbalance
P/E Ratio has 2487 (47.8%) missing valuesMissing
Beta has 377 (7.2%) missing valuesMissing
Performance (52 weeks) has 206 (4.0%) missing valuesMissing
Chiffre d'affaires has 620 (11.9%) missing valuesMissing
Résultat net has 142 (2.7%) missing valuesMissing
Sector has 56 (1.1%) missing valuesMissing
Industry has 56 (1.1%) missing valuesMissing
Price 52 Weeks Ago has 177 (3.4%) missing valuesMissing
Total assets has 160 (3.1%) missing valuesMissing
EPS Annual has 72 (1.4%) missing valuesMissing
Dividend Per Share Annual has 2663 (51.2%) missing valuesMissing
EBITDA CAGR (5y) has 2733 (52.5%) missing valuesMissing
EBITDA has 1134 (21.8%) missing valuesMissing
ROI Annual has 101 (1.9%) missing valuesMissing
Ratio Debt/Equity (Annual) has 139 (2.7%) missing valuesMissing
Dividend Yield Indicated Annual has 3417 (65.6%) missing valuesMissing
Price is highly skewed (γ1 = 28.4746071)Skewed
Market Cap (in M) is highly skewed (γ1 = 26.01858778)Skewed
P/E Ratio is highly skewed (γ1 = 34.04712074)Skewed
Volume 52 weeks is highly skewed (γ1 = 45.95877214)Skewed
Volume 1 month is highly skewed (γ1 = 28.59005039)Skewed
52 Weeks High is highly skewed (γ1 = 36.18051647)Skewed
52 Weeks Low is highly skewed (γ1 = 24.52086075)Skewed
Chiffre d'affaires is highly skewed (γ1 = 66.95276587)Skewed
Résultat net is highly skewed (γ1 = -71.12435832)Skewed
Price 52 Weeks Ago is highly skewed (γ1 = 35.41533371)Skewed
EPS Annual is highly skewed (γ1 = -38.88390559)Skewed
Dividend Per Share Annual is highly skewed (γ1 = 50.01283392)Skewed
EBITDA CAGR (5y) is highly skewed (γ1 = 49.71668423)Skewed
EBITDA is highly skewed (γ1 = -63.47869814)Skewed
ROI Annual is highly skewed (γ1 = -54.6172251)Skewed
Ratio Debt/Equity (Annual) is highly skewed (γ1 = 36.19811775)Skewed
Dividend Per Share Annual has 466 (9.0%) zerosZeros
Ratio Debt/Equity (Annual) has 1073 (20.6%) zerosZeros

Reproduction

Analysis started2024-08-12 17:39:25.100781
Analysis finished2024-08-12 17:40:06.368159
Duration41.27 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

Symbol
Text

Distinct5203
Distinct (%)100.0%
Missing2
Missing (%)< 0.1%
Memory size308.1 KiB
2024-08-12T19:40:06.614660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.6029214
Min length1

Characters and Unicode

Total characters18746
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5203 ?
Unique (%)100.0%

Sample

1st rowTRNS
2nd rowACRV
3rd rowCOLM
4th rowZCMD
5th rowMOVE
ValueCountFrequency (%)
trns 1
 
< 0.1%
snax 1
 
< 0.1%
colm 1
 
< 0.1%
zcmd 1
 
< 0.1%
move 1
 
< 0.1%
nmih 1
 
< 0.1%
gnta 1
 
< 0.1%
allr 1
 
< 0.1%
rrbi 1
 
< 0.1%
cndt 1
 
< 0.1%
Other values (5193) 5193
99.8%
2024-08-12T19:40:07.016241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 1322
 
7.1%
C 1298
 
6.9%
T 1239
 
6.6%
S 1205
 
6.4%
R 1175
 
6.3%
N 1083
 
5.8%
I 960
 
5.1%
L 931
 
5.0%
M 885
 
4.7%
E 860
 
4.6%
Other values (16) 7788
41.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18746
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1322
 
7.1%
C 1298
 
6.9%
T 1239
 
6.6%
S 1205
 
6.4%
R 1175
 
6.3%
N 1083
 
5.8%
I 960
 
5.1%
L 931
 
5.0%
M 885
 
4.7%
E 860
 
4.6%
Other values (16) 7788
41.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18746
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1322
 
7.1%
C 1298
 
6.9%
T 1239
 
6.6%
S 1205
 
6.4%
R 1175
 
6.3%
N 1083
 
5.8%
I 960
 
5.1%
L 931
 
5.0%
M 885
 
4.7%
E 860
 
4.6%
Other values (16) 7788
41.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18746
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1322
 
7.1%
C 1298
 
6.9%
T 1239
 
6.6%
S 1205
 
6.4%
R 1175
 
6.3%
N 1083
 
5.8%
I 960
 
5.1%
L 931
 
5.0%
M 885
 
4.7%
E 860
 
4.6%
Other values (16) 7788
41.5%
Distinct5204
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size397.1 KiB
2024-08-12T19:40:07.239961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length76
Median length54
Mean length21.091643
Min length2

Characters and Unicode

Total characters109782
Distinct characters74
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5203 ?
Unique (%)> 99.9%

Sample

1st rowTranscat Inc
2nd rowAcrivon Therapeutics Inc
3rd rowColumbia Sportswear Co
4th rowZhongchao Inc
5th rowMovano Inc
ValueCountFrequency (%)
inc 3140
 
18.7%
corp 896
 
5.3%
ltd 415
 
2.5%
holdings 374
 
2.2%
group 288
 
1.7%
fund 268
 
1.6%
co 193
 
1.1%
income 192
 
1.1%
therapeutics 186
 
1.1%
trust 159
 
0.9%
Other values (5262) 10686
63.6%
2024-08-12T19:40:07.600305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11592
 
10.6%
n 9400
 
8.6%
e 7479
 
6.8%
o 6758
 
6.2%
c 6398
 
5.8%
i 6236
 
5.7%
r 6205
 
5.7%
a 6022
 
5.5%
t 5122
 
4.7%
s 4194
 
3.8%
Other values (64) 40376
36.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 109782
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11592
 
10.6%
n 9400
 
8.6%
e 7479
 
6.8%
o 6758
 
6.2%
c 6398
 
5.8%
i 6236
 
5.7%
r 6205
 
5.7%
a 6022
 
5.5%
t 5122
 
4.7%
s 4194
 
3.8%
Other values (64) 40376
36.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 109782
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11592
 
10.6%
n 9400
 
8.6%
e 7479
 
6.8%
o 6758
 
6.2%
c 6398
 
5.8%
i 6236
 
5.7%
r 6205
 
5.7%
a 6022
 
5.5%
t 5122
 
4.7%
s 4194
 
3.8%
Other values (64) 40376
36.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 109782
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11592
 
10.6%
n 9400
 
8.6%
e 7479
 
6.8%
o 6758
 
6.2%
c 6398
 
5.8%
i 6236
 
5.7%
r 6205
 
5.7%
a 6022
 
5.5%
t 5122
 
4.7%
s 4194
 
3.8%
Other values (64) 40376
36.8%

Price
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3695
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.349616
Minimum0.0503
Maximum8506.24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.8 KiB
2024-08-12T19:40:07.734589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0503
5-th percentile0.62112
Q13.58
median12.25
Q339.32
95-th percentile184.318
Maximum8506.24
Range8506.1897
Interquartile range (IQR)35.74

Descriptive statistics

Standard deviation169.51091
Coefficient of variation (CV)3.6572237
Kurtosis1257.2903
Mean46.349616
Median Absolute Deviation (MAD)10.61
Skewness28.474607
Sum241249.75
Variance28733.95
MonotonicityNot monotonic
2024-08-12T19:40:07.850537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.16 9
 
0.2%
1.4 8
 
0.2%
1.01 8
 
0.2%
1.02 8
 
0.2%
1.65 8
 
0.2%
1.06 8
 
0.2%
1.04 8
 
0.2%
1.6 7
 
0.1%
11.45 7
 
0.1%
1.47 7
 
0.1%
Other values (3685) 5127
98.5%
ValueCountFrequency (%)
0.0503 1
< 0.1%
0.075 1
< 0.1%
0.076 1
< 0.1%
0.0766 1
< 0.1%
0.08 1
< 0.1%
0.0823 1
< 0.1%
0.092 1
< 0.1%
0.0933 1
< 0.1%
0.0945 1
< 0.1%
0.0978 1
< 0.1%
ValueCountFrequency (%)
8506.24 1
< 0.1%
3443.05 1
< 0.1%
3120.25 1
< 0.1%
1974.15 1
< 0.1%
1883.62 1
< 0.1%
1752.25 1
< 0.1%
1701.48 1
< 0.1%
1521.92 1
< 0.1%
1397.26 1
< 0.1%
1259.41 1
< 0.1%

Market Cap (in M)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5200
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11000.458
Minimum0
Maximum3287742.5
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size40.8 KiB
2024-08-12T19:40:08.077725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.8301727
Q193.503234
median553.91131
Q33255.8942
95-th percentile38320.067
Maximum3287742.5
Range3287742.5
Interquartile range (IQR)3162.3909

Descriptive statistics

Standard deviation89671.506
Coefficient of variation (CV)8.151616
Kurtosis802.27443
Mean11000.458
Median Absolute Deviation (MAD)536.63174
Skewness26.018588
Sum57257382
Variance8.0409791 × 109
MonotonicityNot monotonic
2024-08-12T19:40:08.195827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3
 
0.1%
203.84 2
 
< 0.1%
21.11919333 2
 
< 0.1%
44.22 2
 
< 0.1%
101.819299 1
 
< 0.1%
9150.268493 1
 
< 0.1%
187.13 1
 
< 0.1%
1420.384117 1
 
< 0.1%
1300.036875 1
 
< 0.1%
2651.83368 1
 
< 0.1%
Other values (5190) 5190
99.7%
ValueCountFrequency (%)
0 3
0.1%
0.135 1
 
< 0.1%
0.202462083 1
 
< 0.1%
0.290874472 1
 
< 0.1%
0.3662907246 1
 
< 0.1%
0.5117001326 1
 
< 0.1%
0.6849636886 1
 
< 0.1%
0.6859719465 1
 
< 0.1%
0.727851 1
 
< 0.1%
0.7453370722 1
 
< 0.1%
ValueCountFrequency (%)
3287742.486 1
< 0.1%
3017962.34 1
< 0.1%
2581647.096 1
< 0.1%
2024988.839 1
< 0.1%
1752130.051 1
< 0.1%
1309863.215 1
< 0.1%
847457.4806 1
< 0.1%
690133.0242 1
< 0.1%
638928.0644 1
< 0.1%
585534.9078 1
< 0.1%

P/E Ratio
Real number (ℝ)

MISSING  SKEWED 

Distinct2712
Distinct (%)99.8%
Missing2487
Missing (%)47.8%
Infinite0
Infinite (%)0.0%
Mean60.377204
Minimum0.0062
Maximum21839.557
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.8 KiB
2024-08-12T19:40:08.328397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0062
5-th percentile3.10411
Q110.04845
median18.5168
Q334.7352
95-th percentile127.53008
Maximum21839.557
Range21839.551
Interquartile range (IQR)24.68675

Descriptive statistics

Standard deviation513.1656
Coefficient of variation (CV)8.499327
Kurtosis1328.3958
Mean60.377204
Median Absolute Deviation (MAD)10.1093
Skewness34.047121
Sum164105.24
Variance263338.93
MonotonicityNot monotonic
2024-08-12T19:40:08.448463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.4093 2
 
< 0.1%
10.3985 2
 
< 0.1%
16.1532 2
 
< 0.1%
4.4923 2
 
< 0.1%
15.2663 2
 
< 0.1%
5.372 2
 
< 0.1%
74.8157 1
 
< 0.1%
3.0741 1
 
< 0.1%
8.1228 1
 
< 0.1%
60.109 1
 
< 0.1%
Other values (2702) 2702
51.9%
(Missing) 2487
47.8%
ValueCountFrequency (%)
0.0062 1
< 0.1%
0.0203 1
< 0.1%
0.0302 1
< 0.1%
0.0406 1
< 0.1%
0.0578 1
< 0.1%
0.1099 1
< 0.1%
0.1138 1
< 0.1%
0.1795 1
< 0.1%
0.1814 1
< 0.1%
0.1884 1
< 0.1%
ValueCountFrequency (%)
21839.5571 1
< 0.1%
12604.5649 1
< 0.1%
3923.2138 1
< 0.1%
3507.735 1
< 0.1%
2956.059 1
< 0.1%
2646.6441 1
< 0.1%
2163.2004 1
< 0.1%
2088.6392 1
< 0.1%
1789.4863 1
< 0.1%
1524.9309 1
< 0.1%

Beta
Real number (ℝ)

MISSING 

Distinct4827
Distinct (%)> 99.9%
Missing377
Missing (%)7.2%
Infinite0
Infinite (%)0.0%
Mean1.0560383
Minimum-9.529384
Maximum47.4254
Zeros0
Zeros (%)0.0%
Negative521
Negative (%)10.0%
Memory size40.8 KiB
2024-08-12T19:40:08.563441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-9.529384
5-th percentile-0.30892312
Q10.41308804
median0.9198492
Q31.5517692
95-th percentile2.9434535
Maximum47.4254
Range56.954784
Interquartile range (IQR)1.1386811

Descriptive statistics

Standard deviation1.3628532
Coefficient of variation (CV)1.2905339
Kurtosis290.64115
Mean1.0560383
Median Absolute Deviation (MAD)0.562268
Skewness9.1941424
Sum5098.5531
Variance1.8573689
MonotonicityNot monotonic
2024-08-12T19:40:08.677713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.87638956 2
 
< 0.1%
0.9319894 1
 
< 0.1%
0.76148 1
 
< 0.1%
1.289463 1
 
< 0.1%
0.93625027 1
 
< 0.1%
0.90106916 1
 
< 0.1%
0.8942454 1
 
< 0.1%
0.990637 1
 
< 0.1%
0.023227256 1
 
< 0.1%
0.21630187 1
 
< 0.1%
Other values (4817) 4817
92.5%
(Missing) 377
 
7.2%
ValueCountFrequency (%)
-9.529384 1
< 0.1%
-6.9775743 1
< 0.1%
-6.976032 1
< 0.1%
-5.8663783 1
< 0.1%
-5.5417604 1
< 0.1%
-5.3560877 1
< 0.1%
-4.9759016 1
< 0.1%
-4.6895614 1
< 0.1%
-4.5976734 1
< 0.1%
-4.5330396 1
< 0.1%
ValueCountFrequency (%)
47.4254 1
< 0.1%
19.570795 1
< 0.1%
15.053838 1
< 0.1%
11.313087 1
< 0.1%
10.333447 1
< 0.1%
9.652831 1
< 0.1%
9.578695 1
< 0.1%
8.625989 1
< 0.1%
8.4484005 1
< 0.1%
8.446643 1
< 0.1%

Volume 52 weeks
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5200
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1394929.4
Minimum0
Maximum4.6049593 × 108
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size40.8 KiB
2024-08-12T19:40:08.807467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12404.524
Q185787.698
median352927.78
Q31098329.4
95-th percentile5102799.5
Maximum4.6049593 × 108
Range4.6049593 × 108
Interquartile range (IQR)1012541.7

Descriptive statistics

Standard deviation7481321.4
Coefficient of variation (CV)5.3632258
Kurtosis2738.7338
Mean1394929.4
Median Absolute Deviation (MAD)314832.94
Skewness45.958772
Sum7.2606076 × 109
Variance5.597017 × 1013
MonotonicityNot monotonic
2024-08-12T19:40:08.933478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
291535.3175 2
 
< 0.1%
6648.015873 2
 
< 0.1%
82251.5873 2
 
< 0.1%
0 2
 
< 0.1%
225453.9683 2
 
< 0.1%
78841.66667 1
 
< 0.1%
1216939.683 1
 
< 0.1%
13591270.63 1
 
< 0.1%
273965.0794 1
 
< 0.1%
801371.4286 1
 
< 0.1%
Other values (5190) 5190
99.7%
ValueCountFrequency (%)
0 2
< 0.1%
156.5737052 1
< 0.1%
251.984127 1
< 0.1%
329.7619048 1
< 0.1%
343.484127 1
< 0.1%
364.0355731 1
< 0.1%
665.0873016 1
< 0.1%
777.7777778 1
< 0.1%
849.2063492 1
< 0.1%
935.059761 1
< 0.1%
ValueCountFrequency (%)
460495926.5 1
< 0.1%
107475531.3 1
< 0.1%
60843084.92 1
< 0.1%
60546926.98 1
< 0.1%
56703451.59 1
< 0.1%
53449109.52 1
< 0.1%
53207215.08 1
< 0.1%
52205071.83 1
< 0.1%
49901964.06 1
< 0.1%
46498527.38 1
< 0.1%

Volume 1 month
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5118
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1577860.7
Minimum0
Maximum3.4773365 × 108
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size40.8 KiB
2024-08-12T19:40:09.056390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8481.7391
Q172173.913
median339656.52
Q31171152.2
95-th percentile6110533.9
Maximum3.4773365 × 108
Range3.4773365 × 108
Interquartile range (IQR)1098978.3

Descriptive statistics

Standard deviation6847884.2
Coefficient of variation (CV)4.3399801
Kurtosis1294.1895
Mean1577860.7
Median Absolute Deviation (MAD)312639.13
Skewness28.59005
Sum8.2127651 × 109
Variance4.6893518 × 1013
MonotonicityNot monotonic
2024-08-12T19:40:09.178509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81686.95652 3
 
0.1%
52026.08696 3
 
0.1%
51391.30435 3
 
0.1%
23152.17391 3
 
0.1%
13913.04348 2
 
< 0.1%
25865.21739 2
 
< 0.1%
4356.521739 2
 
< 0.1%
1244960.87 2
 
< 0.1%
2143.478261 2
 
< 0.1%
18326.08696 2
 
< 0.1%
Other values (5108) 5181
99.5%
ValueCountFrequency (%)
0 2
< 0.1%
4.545454545 1
< 0.1%
17.39130435 1
< 0.1%
47.82608696 1
< 0.1%
65.2173913 1
< 0.1%
73.91304348 1
< 0.1%
125.0416667 1
< 0.1%
139.1304348 1
< 0.1%
181.8181818 1
< 0.1%
182.6086957 1
< 0.1%
ValueCountFrequency (%)
347733646.8 1
< 0.1%
110366273.9 1
< 0.1%
108802565.2 1
< 0.1%
81539721.74 1
< 0.1%
78406543.48 1
< 0.1%
63759239.13 1
< 0.1%
60902782.61 1
< 0.1%
59622121.74 1
< 0.1%
58253669.57 1
< 0.1%
53438513.04 1
< 0.1%

52 Weeks High
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3864
Distinct (%)74.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.169438
Minimum0.87
Maximum14400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.8 KiB
2024-08-12T19:40:09.308405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.87
5-th percentile2.4918
Q18.7
median18.75
Q353.31
95-th percentile227.846
Maximum14400
Range14399.13
Interquartile range (IQR)44.61

Descriptive statistics

Standard deviation271.24489
Coefficient of variation (CV)4.3629941
Kurtosis1713.4033
Mean62.169438
Median Absolute Deviation (MAD)13.84
Skewness36.180516
Sum323591.93
Variance73573.792
MonotonicityNot monotonic
2024-08-12T19:40:09.427350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 9
 
0.2%
12 8
 
0.2%
13 8
 
0.2%
2.05 8
 
0.2%
4 8
 
0.2%
12.5 7
 
0.1%
11.85 7
 
0.1%
3.6 7
 
0.1%
2.1 7
 
0.1%
32 6
 
0.1%
Other values (3854) 5130
98.6%
ValueCountFrequency (%)
0.87 1
< 0.1%
0.8999 2
< 0.1%
0.9039 1
< 0.1%
0.909 1
< 0.1%
0.93 1
< 0.1%
0.94 1
< 0.1%
0.98 2
< 0.1%
0.99 1
< 0.1%
1.01 1
< 0.1%
1.02 1
< 0.1%
ValueCountFrequency (%)
14400 1
< 0.1%
8700 1
< 0.1%
4144.32 1
< 0.1%
3242.54 1
< 0.1%
2173.01 1
< 0.1%
1905.09 1
< 0.1%
1899.21 1
< 0.1%
1759.76 1
< 0.1%
1670.24 1
< 0.1%
1535.86 1
< 0.1%

52 Weeks Low
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3638
Distinct (%)69.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.073025
Minimum0.0004
Maximum5210.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.8 KiB
2024-08-12T19:40:09.553976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0004
5-th percentile0.44108
Q12.32
median9.8
Q328.2
95-th percentile135.084
Maximum5210.49
Range5210.4896
Interquartile range (IQR)25.88

Descriptive statistics

Standard deviation113.50719
Coefficient of variation (CV)3.4320171
Kurtosis942.82077
Mean33.073025
Median Absolute Deviation (MAD)8.5
Skewness24.520861
Sum172145.1
Variance12883.882
MonotonicityNot monotonic
2024-08-12T19:40:09.680245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.75 13
 
0.2%
1 11
 
0.2%
0.65 10
 
0.2%
1.21 9
 
0.2%
1.04 9
 
0.2%
0.7 9
 
0.2%
1.1 9
 
0.2%
1.5 9
 
0.2%
0.4 8
 
0.2%
1.25 8
 
0.2%
Other values (3628) 5110
98.2%
ValueCountFrequency (%)
0.0004 1
< 0.1%
0.0131 1
< 0.1%
0.038 1
< 0.1%
0.048 1
< 0.1%
0.056 1
< 0.1%
0.064 1
< 0.1%
0.07 1
< 0.1%
0.0712 1
< 0.1%
0.0734 1
< 0.1%
0.0767 1
< 0.1%
ValueCountFrequency (%)
5210.49 1
< 0.1%
2735.3 1
< 0.1%
2379.02 1
< 0.1%
1401.0101 1
< 0.1%
1295.65 1
< 0.1%
1274.91 1
< 0.1%
1141.04 1
< 0.1%
930.72 1
< 0.1%
860.1 1
< 0.1%
811.99 1
< 0.1%

Exchange
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size316.4 KiB
NASDAQ
3157 
NYSE
2048 

Length

Max length6
Median length6
Mean length5.2130644
Min length4

Characters and Unicode

Total characters27134
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNASDAQ
2nd rowNASDAQ
3rd rowNASDAQ
4th rowNASDAQ
5th rowNASDAQ

Common Values

ValueCountFrequency (%)
NASDAQ 3157
60.7%
NYSE 2048
39.3%

Length

2024-08-12T19:40:09.909086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T19:40:10.029800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
nasdaq 3157
60.7%
nyse 2048
39.3%

Most occurring characters

ValueCountFrequency (%)
A 6314
23.3%
N 5205
19.2%
S 5205
19.2%
D 3157
11.6%
Q 3157
11.6%
Y 2048
 
7.5%
E 2048
 
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27134
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 6314
23.3%
N 5205
19.2%
S 5205
19.2%
D 3157
11.6%
Q 3157
11.6%
Y 2048
 
7.5%
E 2048
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27134
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 6314
23.3%
N 5205
19.2%
S 5205
19.2%
D 3157
11.6%
Q 3157
11.6%
Y 2048
 
7.5%
E 2048
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27134
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 6314
23.3%
N 5205
19.2%
S 5205
19.2%
D 3157
11.6%
Q 3157
11.6%
Y 2048
 
7.5%
E 2048
 
7.5%

Performance (52 weeks)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct4990
Distinct (%)99.8%
Missing206
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean-0.0032912484
Minimum-0.99985424
Maximum33.280986
Zeros0
Zeros (%)0.0%
Negative2441
Negative (%)46.9%
Memory size40.8 KiB
2024-08-12T19:40:10.124380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.99985424
5-th percentile-0.85564087
Q1-0.33778981
median0.013992044
Q30.21187696
95-th percentile0.76862214
Maximum33.280986
Range34.28084
Interquartile range (IQR)0.54966677

Descriptive statistics

Standard deviation0.76752171
Coefficient of variation (CV)-233.20079
Kurtosis720.02481
Mean-0.0032912484
Median Absolute Deviation (MAD)0.25668301
Skewness18.09904
Sum-16.452951
Variance0.58908958
MonotonicityNot monotonic
2024-08-12T19:40:10.231776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.7109845435 2
 
< 0.1%
-0.7696988475 2
 
< 0.1%
0.387608379 2
 
< 0.1%
-0.1670839982 2
 
< 0.1%
-0.5009512193 2
 
< 0.1%
-0.8042474026 2
 
< 0.1%
-0.47592853 2
 
< 0.1%
-0.06056350255 2
 
< 0.1%
0.07029265643 2
 
< 0.1%
-0.0004486332649 1
 
< 0.1%
Other values (4980) 4980
95.7%
(Missing) 206
 
4.0%
ValueCountFrequency (%)
-0.9998542374 1
< 0.1%
-0.9994216567 1
< 0.1%
-0.9992965079 1
< 0.1%
-0.9982727312 1
< 0.1%
-0.9979352051 1
< 0.1%
-0.9973795645 1
< 0.1%
-0.9973444564 1
< 0.1%
-0.9966251513 1
< 0.1%
-0.9965653988 1
< 0.1%
-0.9964575223 1
< 0.1%
ValueCountFrequency (%)
33.28098556 1
< 0.1%
8.725687571 1
< 0.1%
7.564530876 1
< 0.1%
7.492914449 1
< 0.1%
6.921500924 1
< 0.1%
5.897939474 1
< 0.1%
5.736607268 1
< 0.1%
5.64065769 1
< 0.1%
5.450054284 1
< 0.1%
5.299123385 1
< 0.1%
Distinct52
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size300.0 KiB
2024-08-12T19:40:10.357833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters10410
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)0.2%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowCN
5th rowUS
ValueCountFrequency (%)
us 4398
84.5%
cn 207
 
4.0%
il 86
 
1.7%
gb 66
 
1.3%
ca 60
 
1.2%
hk 51
 
1.0%
sg 48
 
0.9%
bm 32
 
0.6%
ie 31
 
0.6%
ky 25
 
0.5%
Other values (42) 201
 
3.9%
2024-08-12T19:40:10.580940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 4456
42.8%
U 4430
42.6%
C 296
 
2.8%
N 224
 
2.2%
G 133
 
1.3%
I 128
 
1.2%
L 115
 
1.1%
B 111
 
1.1%
K 86
 
0.8%
A 82
 
0.8%
Other values (15) 349
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10410
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 4456
42.8%
U 4430
42.6%
C 296
 
2.8%
N 224
 
2.2%
G 133
 
1.3%
I 128
 
1.2%
L 115
 
1.1%
B 111
 
1.1%
K 86
 
0.8%
A 82
 
0.8%
Other values (15) 349
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10410
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 4456
42.8%
U 4430
42.6%
C 296
 
2.8%
N 224
 
2.2%
G 133
 
1.3%
I 128
 
1.2%
L 115
 
1.1%
B 111
 
1.1%
K 86
 
0.8%
A 82
 
0.8%
Other values (15) 349
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10410
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 4456
42.8%
U 4430
42.6%
C 296
 
2.8%
N 224
 
2.2%
G 133
 
1.3%
I 128
 
1.2%
L 115
 
1.1%
B 111
 
1.1%
K 86
 
0.8%
A 82
 
0.8%
Other values (15) 349
 
3.4%

Chiffre d'affaires
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct4564
Distinct (%)99.5%
Missing620
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean1.347777 × 1010
Minimum-3.66072 × 108
Maximum3.4011754 × 1013
Zeros0
Zeros (%)0.0%
Negative15
Negative (%)0.3%
Memory size40.8 KiB
2024-08-12T19:40:10.700794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-3.66072 × 108
5-th percentile1876323.4
Q153300000
median4.4814202 × 108
Q32.408489 × 109
95-th percentile1.97816 × 1010
Maximum3.4011754 × 1013
Range3.401212 × 1013
Interquartile range (IQR)2.355189 × 109

Descriptive statistics

Standard deviation5.0415324 × 1011
Coefficient of variation (CV)37.40628
Kurtosis4514.6144
Mean1.347777 × 1010
Median Absolute Deviation (MAD)4.3925702 × 108
Skewness66.952766
Sum6.1795576 × 1013
Variance2.5417049 × 1023
MonotonicityNot monotonic
2024-08-12T19:40:10.817137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500000 4
 
0.1%
6591000064 3
 
0.1%
11942000 2
 
< 0.1%
22233000 2
 
< 0.1%
6853000 2
 
< 0.1%
65000 2
 
< 0.1%
3500000 2
 
< 0.1%
68000 2
 
< 0.1%
1000000 2
 
< 0.1%
143768992 2
 
< 0.1%
Other values (4554) 4562
87.6%
(Missing) 620
 
11.9%
ValueCountFrequency (%)
-366072000 1
< 0.1%
-130852000 1
< 0.1%
-98696000 1
< 0.1%
-75971000 1
< 0.1%
-65000000 1
< 0.1%
-59345000 1
< 0.1%
-38133000 1
< 0.1%
-21382000 1
< 0.1%
-11137000 1
< 0.1%
-9598000 1
< 0.1%
ValueCountFrequency (%)
3.401175374 × 10131
< 0.1%
2.223509078 × 10121
< 0.1%
9.41168001 × 10111
< 0.1%
6.68366209 × 10111
< 0.1%
6.573319782 × 10111
< 0.1%
6.043339981 × 10111
< 0.1%
3.856030106 × 10111
< 0.1%
3.854390067 × 10111
< 0.1%
3.618549924 × 10111
< 0.1%
3.451349893 × 10111
< 0.1%

Résultat net
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct5021
Distinct (%)99.2%
Missing142
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean-1.0826811 × 1010
Minimum-5.7899787 × 1013
Maximum9.3358398 × 1011
Zeros0
Zeros (%)0.0%
Negative2309
Negative (%)44.4%
Memory size40.8 KiB
2024-08-12T19:40:10.945208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-5.7899787 × 1013
5-th percentile-2.7930071 × 108
Q1-32268535
median3584051
Q31.214675 × 108
95-th percentile1.7825561 × 109
Maximum9.3358398 × 1011
Range5.8833371 × 1013
Interquartile range (IQR)1.5373604 × 108

Descriptive statistics

Standard deviation8.1383958 × 1011
Coefficient of variation (CV)-75.168908
Kurtosis5060.1441
Mean-1.0826811 × 1010
Median Absolute Deviation (MAD)60383051
Skewness-71.124358
Sum-5.4816145 × 1013
Variance6.6233486 × 1023
MonotonicityNot monotonic
2024-08-12T19:40:11.061614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
714000000 3
 
0.1%
1052000000 3
 
0.1%
2936000000 2
 
< 0.1%
26037000 2
 
< 0.1%
4487000064 2
 
< 0.1%
975000000 2
 
< 0.1%
81000000 2
 
< 0.1%
-35390000 2
 
< 0.1%
673000000 2
 
< 0.1%
106100000 2
 
< 0.1%
Other values (5011) 5041
96.8%
(Missing) 142
 
2.7%
ValueCountFrequency (%)
-5.789978722 × 10131
< 0.1%
-2.160125542 × 10101
< 0.1%
-1.176899994 × 10101
< 0.1%
-9406706688 1
< 0.1%
-7966970880 1
< 0.1%
-6808999936 1
< 0.1%
-6541000192 1
< 0.1%
-5866999808 1
< 0.1%
-5810999808 1
< 0.1%
-5791000064 1
< 0.1%
ValueCountFrequency (%)
9.335839785 × 10111
< 0.1%
1.224190525 × 10111
< 0.1%
1.019560018 × 10111
< 0.1%
8.813599949 × 10101
< 0.1%
8.765699686 × 10101
< 0.1%
7.992333926 × 10101
< 0.1%
7.974100173 × 10101
< 0.1%
5.221699994 × 10101
< 0.1%
5.143400038 × 10101
< 0.1%
4.441899827 × 10101
< 0.1%

Sector
Categorical

MISSING 

Distinct11
Distinct (%)0.2%
Missing56
Missing (%)1.1%
Memory size357.8 KiB
Financial Services
1168 
Healthcare
1052 
Technology
713 
Industrials
574 
Consumer Cyclical
536 
Other values (6)
1106 

Length

Max length22
Median length18
Mean length13.52282
Min length6

Characters and Unicode

Total characters69629
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndustrials
2nd rowHealthcare
3rd rowConsumer Cyclical
4th rowHealthcare
5th rowHealthcare

Common Values

ValueCountFrequency (%)
Financial Services 1168
22.4%
Healthcare 1052
20.2%
Technology 713
13.7%
Industrials 574
11.0%
Consumer Cyclical 536
10.3%
Real Estate 245
 
4.7%
Consumer Defensive 215
 
4.1%
Communication Services 210
 
4.0%
Energy 184
 
3.5%
Basic Materials 162
 
3.1%

Length

2024-08-12T19:40:11.181978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
services 1378
17.9%
financial 1168
15.2%
healthcare 1052
13.7%
consumer 751
9.8%
technology 713
9.3%
industrials 574
7.5%
cyclical 536
 
7.0%
real 245
 
3.2%
estate 245
 
3.2%
defensive 215
 
2.8%
Other values (5) 808
10.5%

Most occurring characters

ValueCountFrequency (%)
e 7895
11.3%
a 6736
 
9.7%
i 6053
 
8.7%
c 5755
 
8.3%
n 5193
 
7.5%
l 5076
 
7.3%
s 4151
 
6.0%
r 4101
 
5.9%
t 2668
 
3.8%
o 2597
 
3.7%
Other values (21) 19404
27.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 69629
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 7895
11.3%
a 6736
 
9.7%
i 6053
 
8.7%
c 5755
 
8.3%
n 5193
 
7.5%
l 5076
 
7.3%
s 4151
 
6.0%
r 4101
 
5.9%
t 2668
 
3.8%
o 2597
 
3.7%
Other values (21) 19404
27.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 69629
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 7895
11.3%
a 6736
 
9.7%
i 6053
 
8.7%
c 5755
 
8.3%
n 5193
 
7.5%
l 5076
 
7.3%
s 4151
 
6.0%
r 4101
 
5.9%
t 2668
 
3.8%
o 2597
 
3.7%
Other values (21) 19404
27.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 69629
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 7895
11.3%
a 6736
 
9.7%
i 6053
 
8.7%
c 5755
 
8.3%
n 5193
 
7.5%
l 5076
 
7.3%
s 4151
 
6.0%
r 4101
 
5.9%
t 2668
 
3.8%
o 2597
 
3.7%
Other values (21) 19404
27.9%

Industry
Text

MISSING 

Distinct145
Distinct (%)2.8%
Missing56
Missing (%)1.1%
Memory size385.2 KiB
2024-08-12T19:40:11.393120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length40
Median length34
Mean length19.238493
Min length4

Characters and Unicode

Total characters99059
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st rowIndustrial Distribution
2nd rowBiotechnology
3rd rowApparel Manufacturing
4th rowHealth Information Services
5th rowMedical Devices
ValueCountFrequency (%)
2340
 
17.8%
biotechnology 602
 
4.6%
services 466
 
3.5%
management 452
 
3.4%
asset 437
 
3.3%
software 366
 
2.8%
banks 327
 
2.5%
regional 322
 
2.4%
specialty 305
 
2.3%
medical 241
 
1.8%
Other values (189) 7293
55.5%
2024-08-12T19:40:11.741507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 9930
 
10.0%
8002
 
8.1%
i 7023
 
7.1%
t 6980
 
7.0%
a 6842
 
6.9%
n 6767
 
6.8%
o 5527
 
5.6%
s 5302
 
5.4%
r 4785
 
4.8%
c 4363
 
4.4%
Other values (38) 33538
33.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 99059
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 9930
 
10.0%
8002
 
8.1%
i 7023
 
7.1%
t 6980
 
7.0%
a 6842
 
6.9%
n 6767
 
6.8%
o 5527
 
5.6%
s 5302
 
5.4%
r 4785
 
4.8%
c 4363
 
4.4%
Other values (38) 33538
33.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 99059
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 9930
 
10.0%
8002
 
8.1%
i 7023
 
7.1%
t 6980
 
7.0%
a 6842
 
6.9%
n 6767
 
6.8%
o 5527
 
5.6%
s 5302
 
5.4%
r 4785
 
4.8%
c 4363
 
4.4%
Other values (38) 33538
33.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 99059
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 9930
 
10.0%
8002
 
8.1%
i 7023
 
7.1%
t 6980
 
7.0%
a 6842
 
6.9%
n 6767
 
6.8%
o 5527
 
5.6%
s 5302
 
5.4%
r 4785
 
4.8%
c 4363
 
4.4%
Other values (38) 33538
33.9%

Price 52 Weeks Ago
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct4067
Distinct (%)80.9%
Missing177
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean47.01906
Minimum0.30000001
Maximum11250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.8 KiB
2024-08-12T19:40:11.865657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.30000001
5-th percentile1.49
Q16.1754957
median13.660382
Q339.384068
95-th percentile171.18811
Maximum11250
Range11249.7
Interquartile range (IQR)33.208573

Descriptive statistics

Standard deviation213.99657
Coefficient of variation (CV)4.5512729
Kurtosis1653.1908
Mean47.01906
Median Absolute Deviation (MAD)10.476907
Skewness35.415334
Sum236411.83
Variance45794.533
MonotonicityNot monotonic
2024-08-12T19:40:11.972945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.220000029 10
 
0.2%
2.049999952 8
 
0.2%
1.679999948 8
 
0.2%
10.69999981 8
 
0.2%
4.199999809 7
 
0.1%
12 7
 
0.1%
3.019999981 7
 
0.1%
2.599999905 6
 
0.1%
2.559999943 6
 
0.1%
10.60000038 6
 
0.1%
Other values (4057) 4955
95.2%
(Missing) 177
 
3.4%
ValueCountFrequency (%)
0.3000000119 1
< 0.1%
0.400000006 1
< 0.1%
0.4300000072 1
< 0.1%
0.4379999936 1
< 0.1%
0.4799999893 1
< 0.1%
0.5009999871 1
< 0.1%
0.5099999905 1
< 0.1%
0.5170000196 1
< 0.1%
0.5210062861 1
< 0.1%
0.5320000052 1
< 0.1%
ValueCountFrequency (%)
11250 1
< 0.1%
6156.72998 1
< 0.1%
3456 1
< 0.1%
3190.70166 1
< 0.1%
2483.830078 1
< 0.1%
1566.987183 1
< 0.1%
1506.199951 1
< 0.1%
1464.240601 1
< 0.1%
1330 1
< 0.1%
1239.800049 1
< 0.1%

Currency
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct24
Distinct (%)0.5%
Missing49
Missing (%)0.9%
Memory size304.9 KiB
USD
4929 
CNY
 
111
EUR
 
33
BRL
 
12
CAD
 
11
Other values (19)
 
60

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15468
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)0.2%

Sample

1st rowUSD
2nd rowUSD
3rd rowUSD
4th rowUSD
5th rowUSD

Common Values

ValueCountFrequency (%)
USD 4929
94.7%
CNY 111
 
2.1%
EUR 33
 
0.6%
BRL 12
 
0.2%
CAD 11
 
0.2%
HKD 10
 
0.2%
GBP 9
 
0.2%
SGD 6
 
0.1%
AUD 6
 
0.1%
JPY 5
 
0.1%
Other values (14) 24
 
0.5%
(Missing) 49
 
0.9%

Length

2024-08-12T19:40:12.068575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
usd 4929
95.6%
cny 111
 
2.2%
eur 33
 
0.6%
brl 12
 
0.2%
cad 11
 
0.2%
hkd 10
 
0.2%
gbp 9
 
0.2%
sgd 6
 
0.1%
aud 6
 
0.1%
jpy 5
 
0.1%
Other values (14) 24
 
0.5%

Most occurring characters

ValueCountFrequency (%)
D 4970
32.1%
U 4969
32.1%
S 4936
31.9%
C 126
 
0.8%
N 119
 
0.8%
Y 119
 
0.8%
R 54
 
0.3%
E 36
 
0.2%
B 22
 
0.1%
A 18
 
0.1%
Other values (14) 99
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15468
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 4970
32.1%
U 4969
32.1%
S 4936
31.9%
C 126
 
0.8%
N 119
 
0.8%
Y 119
 
0.8%
R 54
 
0.3%
E 36
 
0.2%
B 22
 
0.1%
A 18
 
0.1%
Other values (14) 99
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15468
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 4970
32.1%
U 4969
32.1%
S 4936
31.9%
C 126
 
0.8%
N 119
 
0.8%
Y 119
 
0.8%
R 54
 
0.3%
E 36
 
0.2%
B 22
 
0.1%
A 18
 
0.1%
Other values (14) 99
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15468
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 4970
32.1%
U 4969
32.1%
S 4936
31.9%
C 126
 
0.8%
N 119
 
0.8%
Y 119
 
0.8%
R 54
 
0.3%
E 36
 
0.2%
B 22
 
0.1%
A 18
 
0.1%
Other values (14) 99
 
0.6%

Total assets
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct5029
Distinct (%)99.7%
Missing160
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean1.5744943 × 108
Minimum1024
Maximum1.52041 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.8 KiB
2024-08-12T19:40:12.269456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1024
5-th percentile3664160
Q117058400
median47441000
Q31.22361 × 108
95-th percentile5.71901 × 108
Maximum1.52041 × 1010
Range1.5204099 × 1010
Interquartile range (IQR)1.053026 × 108

Descriptive statistics

Standard deviation5.2438933 × 108
Coefficient of variation (CV)3.3305255
Kurtosis237.57717
Mean1.5744943 × 108
Median Absolute Deviation (MAD)37009100
Skewness12.703754
Sum7.9433237 × 1011
Variance2.7498417 × 1017
MonotonicityNot monotonic
2024-08-12T19:40:12.380940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30000000 2
 
< 0.1%
21921800 2
 
< 0.1%
46608800 2
 
< 0.1%
6374540 2
 
< 0.1%
37457200 2
 
< 0.1%
100625000 2
 
< 0.1%
57145700 2
 
< 0.1%
14000000 2
 
< 0.1%
144976992 2
 
< 0.1%
28483600 2
 
< 0.1%
Other values (5019) 5025
96.5%
(Missing) 160
 
3.1%
ValueCountFrequency (%)
1024 1
< 0.1%
12443 1
< 0.1%
113809 1
< 0.1%
164495 1
< 0.1%
228025 1
< 0.1%
333008 1
< 0.1%
360600 1
< 0.1%
376141 1
< 0.1%
393449 1
< 0.1%
396368 1
< 0.1%
ValueCountFrequency (%)
1.52041001 × 10101
< 0.1%
1.049559962 × 10101
< 0.1%
8910760000 1
< 0.1%
8043539968 1
< 0.1%
7759580160 1
< 0.1%
7433039872 1
< 0.1%
7170240000 1
< 0.1%
6478000000 1
< 0.1%
6365200000 1
< 0.1%
5858999808 1
< 0.1%

EPS Annual
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct5003
Distinct (%)97.5%
Missing72
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean-10.428331
Minimum-27482.559
Maximum11880.286
Zeros5
Zeros (%)0.1%
Negative2410
Negative (%)46.3%
Memory size40.8 KiB
2024-08-12T19:40:12.498055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-27482.559
5-th percentile-8.86228
Q1-1.1455
median0.0964
Q32.0662
95-th percentile9.07822
Maximum11880.286
Range39362.844
Interquartile range (IQR)3.2117

Descriptive statistics

Standard deviation555.59309
Coefficient of variation (CV)-53.277278
Kurtosis1968.5933
Mean-10.428331
Median Absolute Deviation (MAD)1.5453
Skewness-38.883906
Sum-53528.623
Variance308683.69
MonotonicityNot monotonic
2024-08-12T19:40:12.605363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5
 
0.1%
0.0198 3
 
0.1%
-0.0396 3
 
0.1%
-0.0212 3
 
0.1%
0.6003 3
 
0.1%
-0.875 2
 
< 0.1%
0.4738 2
 
< 0.1%
-2.5297 2
 
< 0.1%
0.0099 2
 
< 0.1%
0.3131 2
 
< 0.1%
Other values (4993) 5106
98.1%
(Missing) 72
 
1.4%
ValueCountFrequency (%)
-27482.5587 1
< 0.1%
-24710.4779 1
< 0.1%
-5086.0393 1
< 0.1%
-2997.8 1
< 0.1%
-2181 1
< 0.1%
-1952.8796 1
< 0.1%
-1586.1632 1
< 0.1%
-1564.215 1
< 0.1%
-1018.2512 1
< 0.1%
-1008.1846 1
< 0.1%
ValueCountFrequency (%)
11880.2857 1
< 0.1%
4380.6219 1
< 0.1%
2241.184 1
< 0.1%
788.6044 1
< 0.1%
463.3511 1
< 0.1%
201.4798 1
< 0.1%
149.2047 1
< 0.1%
133.0526 1
< 0.1%
117.4103 1
< 0.1%
92.8812 1
< 0.1%

Dividend Per Share Annual
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1960
Distinct (%)77.1%
Missing2663
Missing (%)51.2%
Infinite0
Infinite (%)0.0%
Mean2.5690946
Minimum0
Maximum2950.8354
Zeros466
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size40.8 KiB
2024-08-12T19:40:12.722298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.11405
median0.72215
Q31.60335
95-th percentile4.720395
Maximum2950.8354
Range2950.8354
Interquartile range (IQR)1.4893

Descriptive statistics

Standard deviation58.660602
Coefficient of variation (CV)22.833181
Kurtosis2514.125
Mean2.5690946
Median Absolute Deviation (MAD)0.6845
Skewness50.012834
Sum6530.6384
Variance3441.0662
MonotonicityNot monotonic
2024-08-12T19:40:12.832199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 466
 
9.0%
0.2 4
 
0.1%
0.0002 4
 
0.1%
1 3
 
0.1%
0.04 3
 
0.1%
0.2401 3
 
0.1%
0.2402 3
 
0.1%
1.36 3
 
0.1%
0.24 3
 
0.1%
0.6149 3
 
0.1%
Other values (1950) 2047
39.3%
(Missing) 2663
51.2%
ValueCountFrequency (%)
0 466
9.0%
0.0001 1
 
< 0.1%
0.0002 4
 
0.1%
0.0003 1
 
< 0.1%
0.0004 2
 
< 0.1%
0.0005 1
 
< 0.1%
0.0008 2
 
< 0.1%
0.0009 1
 
< 0.1%
0.001 1
 
< 0.1%
0.0012 1
 
< 0.1%
ValueCountFrequency (%)
2950.8354 1
< 0.1%
140 1
< 0.1%
134.0642 1
< 0.1%
26.5818 1
< 0.1%
25.0042 1
< 0.1%
23.5369 1
< 0.1%
20.335 1
< 0.1%
20.2984 1
< 0.1%
19.7907 1
< 0.1%
19.5652 1
< 0.1%

EBITDA CAGR (5y)
Real number (ℝ)

MISSING  SKEWED 

Distinct1997
Distinct (%)80.8%
Missing2733
Missing (%)52.5%
Infinite0
Infinite (%)0.0%
Mean85.139422
Minimum-81.41
Maximum189631.11
Zeros0
Zeros (%)0.0%
Negative735
Negative (%)14.1%
Memory size40.8 KiB
2024-08-12T19:40:12.947813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-81.41
5-th percentile-21.396
Q1-2.12
median6.41
Q316.01
95-th percentile41.6115
Maximum189631.11
Range189712.52
Interquartile range (IQR)18.13

Descriptive statistics

Standard deviation3813.936
Coefficient of variation (CV)44.796357
Kurtosis2471.8324
Mean85.139422
Median Absolute Deviation (MAD)9.125
Skewness49.716684
Sum210464.65
Variance14546108
MonotonicityNot monotonic
2024-08-12T19:40:13.058162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.62 6
 
0.1%
-8.31 5
 
0.1%
5.74 4
 
0.1%
4.45 4
 
0.1%
3.3 4
 
0.1%
1.65 4
 
0.1%
5.66 4
 
0.1%
11.12 4
 
0.1%
11.65 4
 
0.1%
-0.05 4
 
0.1%
Other values (1987) 2429
46.7%
(Missing) 2733
52.5%
ValueCountFrequency (%)
-81.41 1
< 0.1%
-75.69 1
< 0.1%
-64.67 1
< 0.1%
-58.66 1
< 0.1%
-55 1
< 0.1%
-54.22 1
< 0.1%
-51.79 1
< 0.1%
-49.8 1
< 0.1%
-48.62 1
< 0.1%
-48.04 1
< 0.1%
ValueCountFrequency (%)
189631.11 1
< 0.1%
248.75 1
< 0.1%
211.04 1
< 0.1%
208.13 1
< 0.1%
160.43 1
< 0.1%
137.53 1
< 0.1%
135.05 1
< 0.1%
130.94 1
< 0.1%
127.74 1
< 0.1%
123.28 1
< 0.1%

EBITDA
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct4053
Distinct (%)99.6%
Missing1134
Missing (%)21.8%
Infinite0
Infinite (%)0.0%
Mean-6.0733777 × 109
Minimum-3.0704371 × 1013
Maximum1.717253 × 1012
Zeros0
Zeros (%)0.0%
Negative1585
Negative (%)30.5%
Memory size40.8 KiB
2024-08-12T19:40:13.184481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-3.0704371 × 1013
5-th percentile-1.7537901 × 108
Q1-16669000
median35762000
Q34.322345 × 108
95-th percentile4.2354995 × 109
Maximum1.717253 × 1012
Range3.2421624 × 1013
Interquartile range (IQR)4.489035 × 108

Descriptive statistics

Standard deviation4.8204276 × 1011
Coefficient of variation (CV)-79.369797
Kurtosis4044.2946
Mean-6.0733777 × 109
Median Absolute Deviation (MAD)1.0948901 × 108
Skewness-63.478698
Sum-2.4724721 × 1013
Variance2.3236522 × 1023
MonotonicityNot monotonic
2024-08-12T19:40:13.302972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1488999936 2
 
< 0.1%
-21442000 2
 
< 0.1%
102000000 2
 
< 0.1%
450000000 2
 
< 0.1%
280700000 2
 
< 0.1%
-195594752 2
 
< 0.1%
-274000000 2
 
< 0.1%
2894000128 2
 
< 0.1%
728000000 2
 
< 0.1%
-56512000 2
 
< 0.1%
Other values (4043) 4051
77.8%
(Missing) 1134
 
21.8%
ValueCountFrequency (%)
-3.070437097 × 10131
< 0.1%
-1.942801613 × 10101
< 0.1%
-7924198912 1
< 0.1%
-7255211008 1
< 0.1%
-4800000000 1
< 0.1%
-4107000064 1
< 0.1%
-2722977024 1
< 0.1%
-2179000064 1
< 0.1%
-2116557952 1
< 0.1%
-1929326208 1
< 0.1%
ValueCountFrequency (%)
1.717253046 × 10121
< 0.1%
1.832219935 × 10111
< 0.1%
1.349113037 × 10111
< 0.1%
1.317810012 × 10111
< 0.1%
1.29433002 × 10111
< 0.1%
1.154780037 × 10111
< 0.1%
1.040490004 × 10111
< 0.1%
7.817014477 × 10101
< 0.1%
7.477400371 × 10101
< 0.1%
7.090299699 × 10101
< 0.1%

ROI Annual
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct3661
Distinct (%)71.7%
Missing101
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean-63.254051
Minimum-108880
Maximum12633.08
Zeros5
Zeros (%)0.1%
Negative2358
Negative (%)45.3%
Memory size40.8 KiB
2024-08-12T19:40:13.427239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-108880
5-th percentile-151.3135
Q1-23.5
median1.105
Q38.13
95-th percentile24.0955
Maximum12633.08
Range121513.08
Interquartile range (IQR)31.63

Descriptive statistics

Standard deviation1707.0188
Coefficient of variation (CV)-26.986712
Kurtosis3325.2085
Mean-63.254051
Median Absolute Deviation (MAD)10.455
Skewness-54.617225
Sum-322848.68
Variance2913913.3
MonotonicityNot monotonic
2024-08-12T19:40:13.544869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.53 8
 
0.2%
6.15 7
 
0.1%
4.19 7
 
0.1%
1.17 6
 
0.1%
3.7 6
 
0.1%
8.62 6
 
0.1%
9.32 6
 
0.1%
3.2 6
 
0.1%
3.28 6
 
0.1%
6.2 6
 
0.1%
Other values (3651) 5040
96.8%
(Missing) 101
 
1.9%
ValueCountFrequency (%)
-108880 1
< 0.1%
-41952.85 1
< 0.1%
-29104.88 1
< 0.1%
-9780 1
< 0.1%
-5196.477 1
< 0.1%
-5033.59 1
< 0.1%
-3715.82 1
< 0.1%
-3645.8 1
< 0.1%
-3304.87 1
< 0.1%
-3184.29 1
< 0.1%
ValueCountFrequency (%)
12633.08 1
< 0.1%
2662.67 1
< 0.1%
1875.53 1
< 0.1%
1670 1
< 0.1%
1054.47 1
< 0.1%
737.69 1
< 0.1%
643.41 1
< 0.1%
445.65 1
< 0.1%
432.25 1
< 0.1%
307.73 1
< 0.1%

Ratio Debt/Equity (Annual)
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct3451
Distinct (%)68.1%
Missing139
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean1.9646629
Minimum0
Maximum848.9286
Zeros1073
Zeros (%)20.6%
Negative0
Negative (%)0.0%
Memory size40.8 KiB
2024-08-12T19:40:13.665115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.008425
median0.31985
Q30.98835
95-th percentile5.717875
Maximum848.9286
Range848.9286
Interquartile range (IQR)0.979925

Descriptive statistics

Standard deviation15.985055
Coefficient of variation (CV)8.136284
Kurtosis1701.1077
Mean1.9646629
Median Absolute Deviation (MAD)0.31985
Skewness36.198118
Sum9952.9821
Variance255.52198
MonotonicityNot monotonic
2024-08-12T19:40:13.891577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1073
 
20.6%
0.0002 10
 
0.2%
0.0001 9
 
0.2%
0.0005 7
 
0.1%
0.0006 6
 
0.1%
0.002 6
 
0.1%
0.0008 6
 
0.1%
0.004 6
 
0.1%
0.0032 6
 
0.1%
0.0065 6
 
0.1%
Other values (3441) 3931
75.5%
(Missing) 139
 
2.7%
ValueCountFrequency (%)
0 1073
20.6%
0.0001 9
 
0.2%
0.0002 10
 
0.2%
0.0003 2
 
< 0.1%
0.0004 5
 
0.1%
0.0005 7
 
0.1%
0.0006 6
 
0.1%
0.0007 2
 
< 0.1%
0.0008 6
 
0.1%
0.0009 1
 
< 0.1%
ValueCountFrequency (%)
848.9286 1
< 0.1%
439.3673 1
< 0.1%
262.3333 1
< 0.1%
255.8918 1
< 0.1%
181.8148 1
< 0.1%
165.6711 1
< 0.1%
136.576 1
< 0.1%
114.4921 1
< 0.1%
102.7736 1
< 0.1%
99.25 1
< 0.1%

Dividend Yield Indicated Annual
Real number (ℝ)

MISSING 

Distinct1723
Distinct (%)96.4%
Missing3417
Missing (%)65.6%
Infinite0
Infinite (%)0.0%
Mean3.6397948
Minimum0
Maximum37.974686
Zeros52
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size40.8 KiB
2024-08-12T19:40:13.998457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.29014166
Q11.3074151
median2.6693502
Q34.4594362
95-th percentile11.382296
Maximum37.974686
Range37.974686
Interquartile range (IQR)3.1520212

Descriptive statistics

Standard deviation3.6877001
Coefficient of variation (CV)1.0131615
Kurtosis13.639232
Mean3.6397948
Median Absolute Deviation (MAD)1.5035975
Skewness2.8080064
Sum6507.9531
Variance13.599132
MonotonicityNot monotonic
2024-08-12T19:40:14.103728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 52
 
1.0%
2.3501763 2
 
< 0.1%
3.937008 2
 
< 0.1%
9.208103 2
 
< 0.1%
1.7857141 2
 
< 0.1%
3.0464585 2
 
< 0.1%
0.44444445 2
 
< 0.1%
3.508772 2
 
< 0.1%
5.0377836 2
 
< 0.1%
1.7621145 2
 
< 0.1%
Other values (1713) 1718
33.0%
(Missing) 3417
65.6%
ValueCountFrequency (%)
0 52
1.0%
0.01611344 1
 
< 0.1%
0.02446483 1
 
< 0.1%
0.02486 1
 
< 0.1%
0.03518154 1
 
< 0.1%
0.0404408 1
 
< 0.1%
0.058823526 1
 
< 0.1%
0.061462812 1
 
< 0.1%
0.08841733 1
 
< 0.1%
0.09412973 1
 
< 0.1%
ValueCountFrequency (%)
37.974686 1
< 0.1%
36.06557 1
< 0.1%
33.41772 1
< 0.1%
27.79828 1
< 0.1%
25.14142 1
< 0.1%
22.988506 1
< 0.1%
21.467888 1
< 0.1%
20.3789 1
< 0.1%
19.793814 1
< 0.1%
19.59184 1
< 0.1%

Interactions

2024-08-12T19:40:03.441578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:26.230673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:28.018908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:29.935223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:31.742817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:33.599597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:35.512650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:37.552892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:39.491007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:41.503243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:43.522720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:45.478445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:47.543911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:49.571474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:51.599810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:53.485175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:55.494557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:57.565355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:59.499319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:40:01.469508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:40:03.537764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:26.314692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:28.103478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:30.012544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:31.828343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:33.682074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:35.604078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:37.640742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:39.574589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:41.591685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:43.617898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:45.569604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:47.625954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:49.658847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:51.687225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:53.581607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:55.587638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:57.651739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:59.582867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:40:01.552806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:40:03.631818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:26.406408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:28.195394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:30.096740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:31.923269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:33.773142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:35.706817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:37.744517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:39.764985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:41.684095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:43.725114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:45.763612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:47.727256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:49.758939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:51.785236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:53.674984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:55.690909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:57.747046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:59.787043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:40:01.654006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:40:03.726580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:26.488031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:28.290092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:30.174885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:32.011675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:33.857808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:35.794088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:37.831900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:39.852596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:41.767225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:43.811239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:45.848001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:47.823917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:49.849278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:51.868599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:53.769665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:55.785082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:57.834777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:59.866986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:40:01.738562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:40:03.813079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:26.578147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:28.384637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:30.266763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:32.100997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:33.951973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:35.888586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:37.934525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:39.944845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:41.856253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:43.914808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:45.944416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:47.919483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:49.947538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:51.962961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:53.856077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:55.887246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:57.928827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:59.956645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:40:01.828219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:40:03.909386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:26.663295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:28.473710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:30.347438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:32.189492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:34.140821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:35.976089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:38.027931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:40.031531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:41.945755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:44.014108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:46.033411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:48.012610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:50.040573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:52.051321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:54.050589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:55.982906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:58.015287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:40:00.042790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:40:01.916447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:40:04.015804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:26.757907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:28.568479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:30.441393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:32.290911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:34.239616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:36.072967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:38.126976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:40.126249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:42.041092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:44.123610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:46.136508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:48.115147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:50.144043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:52.150971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:54.145217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:56.088610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:58.121263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:40:00.140577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:40:02.014954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:40:04.112588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:26.846554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:28.757176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:30.519069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:32.397001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:34.332452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:36.169019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:38.221543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-08-12T19:39:43.246266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:45.189690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:47.261735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:49.295623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:51.325825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:53.211728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:55.202001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:57.270689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:59.215571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:40:01.192035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:40:03.174891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:40:05.188361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:27.845493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:29.758931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:31.561573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:33.422225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:35.336029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:37.355921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:39.304422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:41.311824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:43.337862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:45.287775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:47.361873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:49.384831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:51.413738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:53.304288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:55.305252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:57.372206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:59.310140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:40:01.280984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:40:03.264501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:40:05.279312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:27.927399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:29.843230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:31.646604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:33.509623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:35.419316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:37.447191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:39.398521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:41.403824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:43.426215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:45.381436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:47.450962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:49.471320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:51.501082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:53.393910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:55.398915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:57.461979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:39:59.398275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:40:01.370190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T19:40:03.348959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-08-12T19:40:14.219418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
52 Weeks High52 Weeks LowBetaChiffre d'affairesCurrencyDividend Per Share AnnualDividend Yield Indicated AnnualEBITDAEBITDA CAGR (5y)EPS AnnualExchangeMarket Cap (in M)P/E RatioPerformance (52 weeks)PricePrice 52 Weeks AgoROI AnnualRatio Debt/Equity (Annual)Résultat netSectorTotal assetsVolume 1 monthVolume 52 weeks
52 Weeks High1.0000.873-0.0150.6050.0000.482-0.4880.5630.2500.5320.0060.7140.2120.3310.8870.9630.4960.1940.5220.0000.2080.2950.243
52 Weeks Low0.8731.000-0.1150.6730.0000.540-0.4350.6840.2400.6800.0040.8170.2170.4690.9800.9040.6320.2180.6450.0000.2790.2160.149
Beta-0.015-0.1151.000-0.0530.046-0.105-0.107-0.2580.031-0.2380.1580.0440.114-0.067-0.078-0.059-0.259-0.005-0.2430.1300.1570.2530.274
Chiffre d'affaires0.6050.673-0.0531.0000.9980.329-0.2850.7950.0900.5500.0000.8130.0990.2760.6690.6280.5010.3950.5250.0000.6110.5430.508
Currency0.0000.0000.0460.9981.0000.9970.0000.9980.0000.7070.0500.0000.0000.0000.0000.0000.0000.0000.9980.0590.0000.0000.000
Dividend Per Share Annual0.4820.540-0.1050.3290.9971.0000.2200.5260.0890.5100.0000.448-0.0540.1350.5100.5040.3000.1630.4830.0000.2150.1730.178
Dividend Yield Indicated Annual-0.488-0.435-0.107-0.2850.0000.2201.000-0.085-0.188-0.2950.022-0.303-0.282-0.233-0.476-0.446-0.2680.151-0.2530.134-0.037-0.065-0.053
EBITDA0.5630.684-0.2580.7950.9980.526-0.0851.0000.1140.7370.0000.6800.0050.3780.6640.6040.7120.3930.7860.0000.3900.3050.268
EBITDA CAGR (5y)0.2500.2400.0310.0900.0000.089-0.1880.1141.0000.3570.0000.178-0.1050.1410.2510.2300.371-0.0300.2680.0000.0180.0450.033
EPS Annual0.5320.680-0.2380.5500.7070.510-0.2950.7370.3571.0000.0210.575-0.3690.3740.6570.5710.8520.1570.8150.0180.2380.1200.077
Exchange0.0060.0040.1580.0000.0500.0000.0220.0000.0000.0211.0000.0400.0000.0480.0180.0000.0140.0000.0000.3980.0550.0000.000
Market Cap (in M)0.7140.8170.0440.8130.0000.448-0.3030.6800.1780.5750.0401.0000.2390.4840.8340.7170.5100.3050.5450.0260.7190.6010.557
P/E Ratio0.2120.2170.1140.0990.000-0.054-0.2820.005-0.105-0.3690.0000.2391.0000.1940.2370.209-0.4110.024-0.0920.0420.1040.1090.097
Performance (52 weeks)0.3310.469-0.0670.2760.0000.135-0.2330.3780.1410.3740.0480.4840.1941.0000.5570.2230.3490.0880.3900.0190.1800.0480.010
Price0.8870.980-0.0780.6690.0000.510-0.4760.6640.2510.6570.0180.8340.2370.5571.0000.8890.6060.2200.6240.0000.2890.2430.174
Price 52 Weeks Ago0.9630.904-0.0590.6280.0000.504-0.4460.6040.2300.5710.0000.7170.2090.2230.8891.0000.5410.2150.5520.0210.2100.2750.214
ROI Annual0.4960.632-0.2590.5010.0000.300-0.2680.7120.3710.8520.0140.510-0.4110.3490.6060.5411.0000.1140.7310.0430.1800.0700.034
Ratio Debt/Equity (Annual)0.1940.218-0.0050.3950.0000.1630.1510.393-0.0300.1570.0000.3050.0240.0880.2200.2150.1141.0000.1720.0170.2730.2520.238
Résultat net0.5220.645-0.2430.5250.9980.483-0.2530.7860.2680.8150.0000.545-0.0920.3900.6240.5520.7310.1721.0000.0000.2200.1440.105
Sector0.0000.0000.1300.0000.0590.0000.1340.0000.0000.0180.3980.0260.0420.0190.0000.0210.0430.0170.0001.0000.0450.0000.020
Total assets0.2080.2790.1570.6110.0000.215-0.0370.3900.0180.2380.0550.7190.1040.1800.2890.2100.1800.2730.2200.0451.0000.7670.771
Volume 1 month0.2950.2160.2530.5430.0000.173-0.0650.3050.0450.1200.0000.6010.1090.0480.2430.2750.0700.2520.1440.0000.7671.0000.938
Volume 52 weeks0.2430.1490.2740.5080.0000.178-0.0530.2680.0330.0770.0000.5570.0970.0100.1740.2140.0340.2380.1050.0200.7710.9381.000

Missing values

2024-08-12T19:40:05.548657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-12T19:40:05.868191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-08-12T19:40:06.150555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

SymbolCompany NamePriceMarket Cap (in M)P/E RatioBetaVolume 52 weeksVolume 1 month52 Weeks High52 Weeks LowExchangePerformance (52 weeks)CountryChiffre d'affairesRésultat netSectorIndustryPrice 52 Weeks AgoCurrencyTotal assetsEPS AnnualDividend Per Share AnnualEBITDA CAGR (5y)EBITDAROI AnnualRatio Debt/Equity (Annual)Dividend Yield Indicated Annual
0TRNSTranscat Inc116.29001051.55269274.81570.9319895.090984e+047.298288e+04147.11584.450NASDAQ0.211360US2.655900e+0815106000.0IndustrialsIndustrial Distribution96.050003USD2.553100e+071.6340NaN14.8836794000.05.950.0185NaN
1ACRVAcrivon Therapeutics Inc7.1800217.991134NaN0.7118532.340076e+057.426779e+0412.8503.190NASDAQ-0.381850USNaN-64118000.0HealthcareBiotechnology11.600000USD1.320950e+08-2.7352NaNNaN-69743000.0-49.830.0000NaN
2COLMColumbia Sportswear Co81.83504781.35748718.83610.6283734.619039e+055.568954e+0587.23066.010NASDAQ0.098148US3.385903e+09227407008.0Consumer CyclicalApparel Manufacturing74.540024USD1.250472e+094.09291.20301.65421724992.012.970.00001.491239
3ZCMDZhongchao Inc1.490012.008726NaN-1.4798833.614145e+051.164410e+0612.0001.000NASDAQ-0.873369CN1.943394e+07-11335911.0HealthcareHealth Information Services11.700000USDNaN-4.3550NaNNaN-4902353.0-62.950.0000NaN
4MOVEMovano Inc0.371436.525224NaN1.2164911.513081e+052.487234e+051.4000.266NASDAQ-0.715289US8.520000e+05-27907000.0HealthcareMedical Devices1.300000USD3.505800e+07-0.6339NaNNaN-28069000.0-837.140.0142NaN
5NMIHNMI Holdings Inc37.24002955.0584249.12950.6787915.305636e+055.543996e+0542.00025.640NASDAQ0.280151US6.179140e+08348496992.0Financial ServicesInsurance - Specialty29.110001USDNaN3.8413NaN23.30496424000.013.860.2064NaN
6GNTAGenenta Science SPA4.563581.703059NaN-1.1924995.514391e+032.803921e+046.1002.200NASDAQ-0.199875ITNaN-11645455.0HealthcareBiotechnology5.700000EURNaN-0.6393NaNNaN-11690489.0-57.000.0000NaN
7ALLRAllarity Therapeutics Inc0.14266.204360NaN3.2902102.291709e+069.179877e+0646.6000.138NASDAQ-0.996625USNaN-12823000.0HealthcareBiotechnology41.599998USDNaN-119.6080NaNNaN-16895000.0-285.881.9720NaN
8RRBIRed River Bancshares Inc49.3300341.0500259.66820.6513409.616577e+031.154975e+0458.00042.780NASDAQ-0.004805US1.049270e+0832488000.0Financial ServicesBanks - Regional49.567513USDNaN4.85660.32038.47NaN11.480.00000.736950
9HCKTHackett Group Inc25.5550711.35649220.49760.4041379.765614e+041.270293e+0527.68020.230NASDAQ0.090217US2.974240e+0834749000.0TechnologyInformation Technology Services23.445827USD5.962300e+071.23570.44207.9359527000.027.810.36311.707412
SymbolCompany NamePriceMarket Cap (in M)P/E RatioBetaVolume 52 weeksVolume 1 month52 Weeks High52 Weeks LowExchangePerformance (52 weeks)CountryChiffre d'affairesRésultat netSectorIndustryPrice 52 Weeks AgoCurrencyTotal assetsEPS AnnualDividend Per Share AnnualEBITDA CAGR (5y)EBITDAROI AnnualRatio Debt/Equity (Annual)Dividend Yield Indicated Annual
5195GPORGulfport Energy Corp138.14002501.3117091.70050.9882212.022063e+052.470174e+05165.1300108.8400NYSE0.212501US8.965770e+087.523250e+08EnergyOil & Gas E&P113.989998USD18107100.077.81800.25958.390007.347990e+0851.190.3025NaN
5196HYBNew America High Income Fund Inc8.0400174.7300006.2289NaN5.379206e+048.381739e+048.11036.2300NYSE0.262845US1.944200e+072.812600e+07Financial ServicesAsset Management6.370649USD23374700.01.20330.6769-5.57000NaN10.130.4335NaN
5197BABoeing Co167.9100103460.627412NaN1.1499397.051050e+066.611630e+06267.5400159.7000NYSE-0.288335US7.355700e+10-3.441000e+09IndustrialsAerospace & Defense235.720001USD616166976.0-3.66792.0223-30.590001.322000e+0973.7340.8466NaN
5198IVZInvesco Ltd16.16007272.524128NaN1.2013634.633188e+064.554791e+0618.280012.4800NYSE0.027753US5.814000e+09-3.372000e+08Financial ServicesAsset Management15.724797USD450032000.0-0.21311.3150NaN1.050800e+09-0.420.58995.209657
5199FBPFirst BanCorp19.74003285.27102510.84730.9282111.117185e+061.213457e+0622.120012.7150NYSE0.347341PR8.751180e+083.108070e+08Financial ServicesBanks - Regional14.663054USD163864992.01.70940.566315.24000NaN18.250.10803.258656
5200SNDASonida Senior Living Inc29.3600418.113320NaN0.8845681.800198e+042.690000e+0434.26006.8900NYSE1.992714US2.395880e+08-2.302900e+07HealthcareMedical Care Facilities9.840000USD14240900.0-3.11040.0000-1.160002.636500e+07-3.8064.9138NaN
5201MSDMorgan Stanley Emerging Markets Debt Fund Inc7.6000153.450000NaNNaN8.012738e+047.786522e+047.76006.1100NYSE0.309895US1.492400e+071.699100e+07Financial ServicesAsset Management5.806285USD20190300.0-1.8436NaNNaNNaN-24.250.0052NaN
5202COHRCoherent Corp63.34009656.880542NaN3.4231482.326590e+062.209304e+0680.910028.4700NYSE0.403250US4.598377e+09-4.145670e+08TechnologyScientific & Technical Instruments45.180000USD152460992.0-1.8859NaN30.669277.353950e+08-2.240.5989NaN
5203TWLOTwilio Inc60.41009701.836400NaN1.5282492.782786e+062.422213e+0678.160049.8561NYSE-0.024610US4.239172e+09-5.943220e+08TechnologySoftware - Infrastructure61.930000USD160600000.0-5.5389NaNNaN3.837300e+07-9.460.1034NaN
5204NaNNano Labs Ltd0.282121.119193NaN0.8763902.915353e+055.139130e+044.75000.2801NASDAQ-0.769699CN7.833538e+07-2.543520e+08TechnologySemiconductors1.220000CNY41590200.0-4.2935NaNNaN-1.955948e+0817.160.1031NaN

Duplicate rows

Most frequently occurring

SymbolCompany NamePriceMarket Cap (in M)P/E RatioBetaVolume 52 weeksVolume 1 month52 Weeks High52 Weeks LowExchangePerformance (52 weeks)CountryChiffre d'affairesRésultat netSectorIndustryPrice 52 Weeks AgoCurrencyTotal assetsEPS AnnualDividend Per Share AnnualEBITDA CAGR (5y)EBITDAROI AnnualRatio Debt/Equity (Annual)Dividend Yield Indicated Annual# duplicates
0NaNNano Labs Ltd0.282121.119193NaN0.87639291535.3174651391.3043484.750.2801NASDAQ-0.769699CN78335376.0-254352048.0TechnologySemiconductors1.22CNY41590200.0-4.2935NaNNaN-195594752.017.160.1031NaN2